Modeling of PACU adult high-activity delirium prediction
A highly active and active technology, applied in patient-specific data, health index calculation, medical informatics, etc., can solve the problems of strong destructiveness, low compliance, restlessness in PDHA patients, etc., and achieve the effect of good prediction efficiency.
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Embodiment 1
[0021] Example 1 Modeling of prediction of hyperactive delirium in PACU adults,
[0022] The first step is to select patients who were admitted to the hospital for surgical treatment at the same time and then admitted to the PACU for observation;
[0023] In the second step, according to the diagnostic criteria of PDHA, the patients were divided into two groups: PDHA group and non-PDHA group;
[0024] The third step is to retrospectively collect the data of the two groups of patients using the Access database from the time of the patient's postoperative admission to the PACU to the period when he was discharged from the PACU;
[0025] In the fourth step, the collected research data is randomly matched by stata15 software according to the ratio of 2:1, and divided into training set data and verification set data;
[0026] The fifth step is to use R language for statistics and analysis, and use stepwise logistic regression to screen risk factors, so as to construct a prediction...
Embodiment 2
[0037] Embodiment 2 refers to the appended Figure 1-6 As shown, the present invention is a modeling of PACU adult hyperactive delirium prediction,
[0038] 1. Research object
[0039] (1) Case source
[0040] Adult patients who were admitted to the PACU from January 1, 2018 to December 31, 2019 and underwent postoperative observation were selected from the calendar database and the operating room anesthesia electronic record database.
[0041] (2) Inclusion criteria
[0042] Inclusion criteria: ①Postoperative admission to PACU for observation; ②Age ≧18 years old; ③No cognitive dysfunction before operation; ④Can communicate normally before operation, and can cooperate with the completion of various scores;
[0043] (3) Exclusion criteria
[0044] Exclusion criteria: ① age < 18 years; ② patients with brain parenchymal injury; ③ preoperative cognitive impairment; ④ previous history of mental illness; ⑤ incomplete data records.
[0045] 2. Diagnostic criteria
[0046] Using...
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